Effects of DAM. In addition to narrowing the domain gap between training and test, DAM also prepares powerful input for the subsequent generator. To verify its effectiveness, we use various inputs to retrain the generator and the results are shown in Tab. REF . We have tried to input the white-patched poster (\({x^{mask}}\) ) follow [1]}. In this way, the trained generator degenerates into a white-patch detector, which hardly produces layouts during test. It means that the composition-aware generator cannot handle the data gap without DAM. We also separately use the salient map (\({x^{sal}}\) ) and the inpainted image (\({{x}^{inp}}\) ) as input. As the second row shows, salient-map-only inputs bring a large decrease in composition-relevant metrics for lacking color and textual information. The comparison between the third and fourth rows shows that although only using inpainted images can achieve comparable performance in composition-relevant metrics, the input prepared by DAM (\({\hat{x}}\) ) can make the generator more stable in graphic metrics. We attribute this improvement to the introduction of salient maps.
